Category: Core Agent - Governance
The Validation Agent receives execution plans from the Planning Agent, performs comprehensive policy compliance validation, risk assessment, cost approval, and regulatory framework verification. It acts as the governance gatekeeper that ensures all execution plans meet organizational policies, regulatory requirements, and risk tolerance before proceeding to execution.
The Validation Agent implements Phase 3 (Validation/Evaluation) of the six-phase OpenEAGO architecture:
- Contract Management → Enriched contracts processed
- Planning & Negotiation → Execution plans received
- Validation (Evaluation) ← Validation Agent (This Component)
- Execution ← Approved plans sent to Orchestration Agent
- Context Management ← Validation context propagation
- Communication ← Audit trail and approval notifications
Architecture Flow:
Planning Agent → [Execution Plan] → Validation Agent → [Validation Decision] → Execution Agent → [Phases 4-6] → Response
Security Integration:
- Validates Level 3 security clearance for plan execution
- Enforces multi-level approval workflows for high-risk operations
- Maintains immutable audit trail with blockchain anchoring
Context Integration:
- Receives Plan Context from Planning Agent
- Creates Validation Context for approval decisions
- Propagates enhanced context to execution phase
Core Validation Functions:
- Policy Compliance - Verify adherence to organizational policies and procedures
- Risk Assessment - Evaluate operational, financial, and compliance risks
- Cost Approval - Validate budget constraints and financial authorization
- Regulatory Verification - Ensure compliance with applicable regulatory frameworks
- Human Approval - Route high-risk decisions to human reviewers
- Audit Trail - Maintain immutable record of all validation decisions
{
"execution_id": "exec_a7b8c9d2",
"eago_execution_plan": {
"record_id": "REC_PLAN_B8C9D3",
"record_type": "execution_plan",
"record_status": "pending_validation",
"record_metadata": {
"created_by": "planning_agent",
"created_at": "2026-02-06T10:30:17.456Z",
"version": "0.1.0",
"source_contract_id": "CONTRACT_E7D3A1"
},
"eago_version": "0.1.0",
"message_type": "execution_plan",
"plan_id": "PLAN_F5B2C7",
"contract_id": "CONTRACT_E7D3A1",
"timestamp": "2026-02-06T10:30:17.456Z",
"context_hierarchy": {
"session_context": {
"session_id": "sess_20260206_1030_001",
"user_id": "sarah.clerk@example.com",
"client_id": "EXAMPLE_CRM_SYSTEM",
"authentication": {
"method": "mtls_spiffe",
"spiffe_id": "spiffe://eago.example.com/workload/crm-service",
"security_level": "high",
"groups": ["customer_service", "address_updaters", "uk_operations"]
}
},
"plan_context": {
"plan_id": "PLAN_F5B2C7",
"planning_strategy": "compliance_optimized",
"execution_pattern": "sequential_with_parallel_validation"
}
},
"planning_analysis": {
"strategy_selected": "sequential_with_parallel_validation",
"total_estimated_duration_sec": 480,
"total_estimated_cost_usd": 10.56,
"total_estimated_acu": 3.3,
"confidence_score": 0.92,
"risk_level": "medium",
"compliance_validated": true,
"agents_selected": 4,
"fallback_agents_available": 2
},
"agent_assignments": [
{
"assignment_id": "ASSIGN_001",
"task_id": "TASK_001",
"skill_required": "address_validation",
"primary_agent": {
"agent_id": "address_agent_uk_001",
"agent_name": "UK Address Validation Service",
"selection_score": 0.94,
"selection_reasons": ["high_reliability", "uk_residency", "gdpr_compliant", "cost_efficient"]
},
"estimated_duration_sec": 120,
"estimated_cost_usd": 3.84,
"estimated_acu": 1.2,
"timeout_sec": 180,
"retry_policy": "fixed_retry",
"max_retries": 2
}
],
"compliance_plan": {
"regulatory_frameworks": ["GDPR", "CCPA", "PSD2"],
"data_handling_requirements": {
"encryption_at_rest": true,
"encryption_in_transit": true,
"data_minimization": true,
"purpose_limitation": true,
"retention_period_days": 2555
},
"audit_requirements": {
"audit_trail_required": true,
"immutable_logging": true,
"blockchain_anchoring": true,
"real_time_monitoring": true
}
},
"risk_assessment": {
"overall_risk_level": "medium",
"risk_factors": [
{
"risk_id": "DATA_RESIDENCY",
"risk_level": "low",
"mitigation": "All selected agents operate within UK/EU jurisdiction"
},
{
"risk_id": "AGENT_AVAILABILITY",
"risk_level": "medium",
"mitigation": "Fallback agents configured for all critical tasks"
}
]
}
}
}Compliance Validation Flow:
Plan Input → Policy Lookup → Rule Evaluation → Compliance Scoring → Exception Handling → Compliance Decision
Policy Categories:
- Financial Policies: Budget limits, cost approval thresholds, ACU consumption limits
- Security Policies: Data classification handling, encryption requirements, access controls
- Operational Policies: Agent selection criteria, SLA requirements, performance thresholds
- Regulatory Policies: Framework compliance, data residency, audit requirements
- Business Policies: Approval workflows, escalation paths, notification requirements
Policy Evaluation Structure:
{
"policy_evaluation": {
"policy_id": "POLICY_FINANCIAL_001",
"policy_name": "Customer Data Processing Budget Policy",
"policy_category": "financial",
"evaluation_result": "passed",
"compliance_score": 0.95,
"violations": [],
"warnings": [
{
"warning_id": "WARN_001",
"message": "Cost approaching 80% of approved budget threshold",
"severity": "medium"
}
],
"applied_rules": [
{
"rule_id": "RULE_BUDGET_001",
"rule_description": "PII processing tasks must not exceed $15 per request",
"evaluation": "passed",
"actual_value": 10.56,
"threshold_value": 15.0
}
]
}
}Risk Evaluation Algorithm:
def calculate_risk_score(plan_data):
"""Calculate comprehensive risk score for execution plan."""
risk_factors = {
"financial_risk": calculate_financial_risk(plan_data["cost_estimates"]),
"operational_risk": calculate_operational_risk(plan_data["agent_assignments"]),
"compliance_risk": calculate_compliance_risk(plan_data["regulatory_requirements"]),
"security_risk": calculate_security_risk(plan_data["data_classification"])
}
# Normative weights per SPECIFICATION.md Appendix E.1
# compliance_risk weight MUST NOT be reduced below 0.25
weights = {
"financial_risk": 0.25,
"operational_risk": 0.20,
"compliance_risk": 0.30,
"security_risk": 0.25
}
weighted_score = sum(risk_factors[factor] * weights[factor] for factor in risk_factors)
composite_risk_score = min(1.0, max(0.0, weighted_score))
# Determine risk tier per normative thresholds (SPECIFICATION.md Appendix E.2)
if composite_risk_score >= 0.80:
risk_tier = "critical"
elif composite_risk_score >= 0.60:
risk_tier = "high"
elif composite_risk_score >= 0.40:
risk_tier = "medium"
else:
risk_tier = "low"
return {
"composite_risk_score": composite_risk_score,
"risk_tier": risk_tier,
"dimension_scores": risk_factors,
"dimension_weights": weights
}Risk Tier Thresholds (Normative — SPECIFICATION.md Appendix E.2):
| Tier | composite_risk_score |
Required Action |
|---|---|---|
low |
0.00 – 0.39 | Automated approval eligible |
medium |
0.40 – 0.59 | Proceed with enhanced monitoring |
high |
0.60 – 0.79 | MUST trigger HITL gate; Phase 4 blocked until human approves |
critical |
0.80 – 1.00 | MUST automatically reject; requires board_approval_ref or legal_review_ref for override |
Risk Categories:
- Financial Risk: Cost overrun probability, budget impact assessment, ACU consumption against approved thresholds
- Operational Risk: Agent failure rates, performance degradation likelihood,
sla_breach_probabilityfor each SLO objective (see Performance SLA/SLO and KPIs) - Compliance Risk: Regulatory violation probability, audit finding risk, policy breach likelihood
- Security Risk: Data breach likelihood, unauthorized access risk, identity anomaly indicators
Risk Context Propagation:
The Validation Agent MUST output a risk_context object in the validation decision envelope. This object propagates across all remaining phase transitions (Phases 4–6) and MUST be persisted to the audit trail. See Risk Management Framework for the full cross-phase lifecycle and risk_context schema.
Before approving an execution plan, the Validation Agent MUST verify that every selected agent meets the Agent Registry minimum performance bar defined in Performance SLA/SLO and KPIs and SPECIFICATION.md Appendix D.1. This is a blocking check: plans referencing agents that fail the minimum bar MUST NOT be approved.
KPI Validation Check:
def validate_agent_kpis(execution_plan, agent_registry):
"""
Blocking pre-approval check: verify all selected agents meet the
Agent Registry minimum performance bar.
Returns (passed: bool, violations: list).
"""
violations = []
MINIMUM_BAR = {
"reliability_score": 0.95, # rolling 7d minimum
"availability_pct": 0.9900, # rolling 30d minimum
"error_rate_max": 0.05, # rolling 7d maximum
"latency_p99_degradation": 1.20 # max multiple of declared SLO p99
}
for agent_ref in execution_plan["selected_agents"]:
agent = agent_registry.get_agent(agent_ref["agent_id"])
metrics = agent["performance_metrics"]
if metrics["reliability_score"] < MINIMUM_BAR["reliability_score"]:
violations.append({
"agent_id": agent_ref["agent_id"],
"kpi": "reliability_score",
"required": MINIMUM_BAR["reliability_score"],
"observed": metrics["reliability_score"],
"registry_status": agent["registry_status"]
})
if metrics["availability_pct"] < MINIMUM_BAR["availability_pct"]:
violations.append({
"agent_id": agent_ref["agent_id"],
"kpi": "availability_pct",
"required": MINIMUM_BAR["availability_pct"],
"observed": metrics["availability_pct"],
"registry_status": agent["registry_status"]
})
if metrics["error_rate"] > MINIMUM_BAR["error_rate_max"]:
violations.append({
"agent_id": agent_ref["agent_id"],
"kpi": "error_rate",
"required": f"≤ {MINIMUM_BAR['error_rate_max']}",
"observed": metrics["error_rate"],
"registry_status": agent["registry_status"]
})
return {
"passed": len(violations) == 0,
"violations": violations,
"agents_evaluated": len(execution_plan["selected_agents"])
}Integration with Validation Decision:
If validate_agent_kpis returns passed: false, the Validation Agent MUST set validation_status: "rejected" with rejection_reason: "agent_kpi_minimum_bar_not_met" regardless of risk score or compliance status. This rule cannot be waived by HITL approval alone; the Planning Agent must re-select compliant agents and resubmit the plan.
Approval Decision Matrix:
Risk Level | Cost Threshold | Approval Required
Low | < $5.00 | Automatic Approval
Low | $5.00-$25.00 | Supervisor Approval
Medium | < $10.00 | Supervisor Approval
Medium | $10.00-$50.00 | Manager Approval
High | Any Amount | Director Approval + Security Review
Critical | Any Amount | Board Approval + Legal Review
Human Approval Triggers:
- Total estimated cost exceeds delegated authority limits
- High or critical risk assessment score
- New regulatory framework or jurisdiction
- Cross-border data transfer requirements
- Sensitive data classification (PHI, financial, intellectual property)
- Agent failure rate above acceptable threshold
The Validation Agent integrates Large Language Models (LLMs) to enhance policy compliance validation, risk assessment accuracy, and decision-making quality. This multi-LLM approach combines rule-based validation with intelligent analysis to deliver superior governance outcomes and adaptive compliance assessment.
llm_enhanced_validation:
architecture: "multi_llm_consensus_validation"
confidence_threshold: 0.90
consensus_requirement: "majority_agreement_with_escalation"
fallback_strategy: "rule_based_validation"
llm_configuration:
specialized_models:
- model_id: "claude-3-sonnet"
weight: 0.35
specialization: ["compliance_analysis", "regulatory_interpretation", "risk_reasoning"]
- model_id: "gpt-4-turbo"
weight: 0.35
specialization: ["policy_analysis", "decision_synthesis", "exception_handling"]
- model_id: "llama-3-70b"
weight: 0.30
specialization: ["cost_analysis", "operational_risk", "pattern_recognition"]
validation_rules:
minimum_consensus: 0.75
confidence_threshold: 0.85
escalation_threshold: 0.60
performance_optimization:
parallel_analysis: true
request_timeout: 25000
retry_attempts: 2
result_caching: true
cache_duration: 1800Multi-Dimensional Policy Validation Pipeline:
async def llm_enhanced_policy_compliance(execution_plan, policy_framework):
"""Enhanced policy compliance analysis using multiple LLM perspectives."""
# Traditional rule-based compliance (baseline)
baseline_compliance = await rule_based_policy_compliance(execution_plan, policy_framework)
# Parallel LLM policy analysis across different domains
llm_analysis_tasks = [
analyze_policy_intent_compliance_with_llm(execution_plan, policy_framework),
assess_policy_exception_eligibility_with_llm(execution_plan, policy_framework),
evaluate_policy_conflict_resolution_with_llm(execution_plan, policy_framework),
identify_hidden_policy_implications_with_llm(execution_plan, policy_framework),
suggest_compliance_optimizations_with_llm(execution_plan, policy_framework),
analyze_regulatory_policy_alignment_with_llm(execution_plan, policy_framework)
]
llm_compliance_insights = await asyncio.gather(*llm_analysis_tasks)
# Generate consensus compliance assessment
enhanced_compliance = merge_compliance_analysis(baseline_compliance, llm_compliance_insights)
return {
"baseline_compliance": baseline_compliance,
"llm_insights": llm_compliance_insights,
"enhanced_assessment": enhanced_compliance,
"compliance_confidence": calculate_compliance_confidence(llm_compliance_insights)
}LLM Policy Compliance Results:
{
"llm_policy_compliance_analysis": {
"analysis_timestamp": "2026-02-06T10:30:18.234Z",
"processing_duration_ms": 1923,
"consensus_confidence": 0.91,
"policy_intent_analysis": {
"overall_intent_alignment": 0.87,
"policy_category_alignment": {
"financial_policies": {
"alignment_score": 0.92,
"intent_assessment": "strong_alignment",
"key_findings": [
"Cost control objectives well-supported by tiered agent selection",
"Budget transparency maintained through detailed cost breakdown",
"ROI optimization evident in efficiency-focused agent choices"
],
"potential_concerns": [
"Aggressive cost optimization may impact service quality margins"
]
},
"security_policies": {
"alignment_score": 0.95,
"intent_assessment": "excellent_alignment",
"key_findings": [
"Data protection objectives exceeded through enhanced encryption",
"Access control principles properly implemented",
"Audit trail requirements comprehensively addressed"
],
"potential_concerns": []
},
"operational_policies": {
"alignment_score": 0.83,
"intent_assessment": "good_alignment",
"key_findings": [
"Service level objectives achievable with selected agents",
"Fallback strategies align with business continuity goals"
],
"potential_concerns": [
"Single jurisdiction dependency may limit operational flexibility"
]
}
}
}
}
}Multi-LLM Risk Evaluation Framework:
async def llm_enhanced_risk_assessment(execution_plan, risk_context):
"""Comprehensive risk assessment using multiple LLM risk analysis perspectives."""
# Traditional quantitative risk assessment (baseline)
baseline_risk = await quantitative_risk_assessment(execution_plan)
# Parallel LLM risk analysis across multiple dimensions
llm_risk_tasks = [
analyze_operational_risk_patterns_with_llm(execution_plan, risk_context),
assess_emerging_risk_factors_with_llm(execution_plan, risk_context),
evaluate_cascading_risk_scenarios_with_llm(execution_plan, risk_context),
identify_black_swan_risk_potential_with_llm(execution_plan, risk_context),
suggest_risk_mitigation_strategies_with_llm(execution_plan, risk_context),
analyze_risk_interdependencies_with_llm(execution_plan, risk_context)
]
llm_risk_insights = await asyncio.gather(*llm_risk_tasks)
# Generate consensus risk assessment
enhanced_risk_assessment = synthesize_risk_analysis(baseline_risk, llm_risk_insights)
return {
"baseline_risk_assessment": baseline_risk,
"llm_risk_insights": llm_risk_insights,
"enhanced_risk_profile": enhanced_risk_assessment,
"risk_confidence": calculate_risk_confidence(llm_risk_insights)
}
}Intelligent Approval Synthesis Engine:
async def llm_enhanced_approval_decision(validation_results, approval_context):
"""Generate approval decisions using LLM synthesis of complex factors."""
# Traditional rule-based approval logic (baseline)
baseline_decision = await rule_based_approval_decision(validation_results)
# Multi-LLM approval analysis
llm_approval_tasks = [
synthesize_approval_factors_with_llm(validation_results, approval_context),
analyze_approval_precedents_with_llm(validation_results, approval_context),
assess_exception_justifications_with_llm(validation_results, approval_context),
evaluate_conditional_approval_options_with_llm(validation_results, approval_context),
generate_approval_rationale_with_llm(validation_results, approval_context)
]
llm_approval_insights = await asyncio.gather(*llm_approval_tasks)
# Generate consensus approval decision
enhanced_approval_decision = synthesize_approval_decision(baseline_decision, llm_approval_insights)
return {
"baseline_decision": baseline_decision,
"llm_insights": llm_approval_insights,
"final_decision": enhanced_approval_decision,
"decision_confidence": calculate_decision_confidence(llm_approval_insights)
}
}This comprehensive LLM enhancement transforms the Validation Agent from a rule-based compliance checker into an intelligent governance system that combines deterministic validation with advanced reasoning, contextual analysis, and adaptive decision-making capabilities while maintaining the reliability and auditability required for enterprise governance.
Validation Input (from Planning Agent):
{
"execution_id": "exec_a7b8c9d2",
"eago_execution_plan": {
// ...complete execution plan structure from above...
}
}Validation Processing Results:
{
"validation_results": {
"validation_timestamp": "2026-02-06T10:30:18.789Z",
"validation_duration_ms": 125,
"validation_agent": "validation_agent_001",
"policy_compliance": {
"overall_compliance": "passed",
"compliance_score": 0.94,
"policies_evaluated": 12,
"policies_passed": 11,
"policies_warned": 1,
"policies_failed": 0,
"compliance_details": [
{
"policy_id": "POLICY_FINANCIAL_001",
"policy_name": "PII Processing Budget Policy",
"result": "passed",
"score": 0.95,
"details": "Cost $10.56 within approved limit of $15.00"
},
{
"policy_id": "POLICY_SECURITY_001",
"policy_name": "Data Encryption Requirements",
"result": "passed",
"score": 1.0,
"details": "All selected agents support required encryption standards"
},
{
"policy_id": "POLICY_GDPR_001",
"policy_name": "EU Data Residency Compliance",
"result": "warned",
"score": 0.85,
"details": "Backup agent located outside preferred UK jurisdiction",
"warning": "Consider UK-only agent selection for enhanced compliance"
}
]
},
"risk_assessment": {
"overall_risk_score": 0.34,
"overall_risk_level": "medium",
"risk_categories": {
"financial_risk": {
"score": 0.25,
"level": "low",
"factors": ["cost_within_budget", "acu_consumption_acceptable"]
},
"operational_risk": {
"score": 0.40,
"level": "medium",
"factors": ["agent_reliability_good", "fallback_available", "timeout_margins_adequate"]
},
"compliance_risk": {
"score": 0.20,
"level": "low",
"factors": ["gdpr_compliant", "encryption_enforced", "audit_trail_enabled"]
},
"security_risk": {
"score": 0.15,
"level": "low",
"factors": ["high_security_level", "mtls_authentication", "data_classification_handled"]
},
"availability_risk": {
"score": 0.30,
"level": "low",
"factors": ["agent_uptime_excellent", "multiple_fallbacks"]
}
},
"mitigation_recommendations": [
"Monitor agent performance during execution",
"Enable real-time cost tracking",
"Activate enhanced audit logging for PII processing"
]
},
"cost_approval": {
"approval_status": "approved",
"total_estimated_cost": 10.56,
"approved_budget_limit": 15.00,
"budget_utilization": 0.704,
"approval_authority": "supervisor_delegation",
"approver_details": {
"approver_type": "automated",
"delegated_limit": 25.00,
"approval_policy": "POLICY_DELEGATION_001"
}
}
}
}Validation Decision Output (for Execution Agent):
{
"execution_id": "exec_a7b8c9d2",
"eago_validation_decision": {
"record_id": "REC_VALIDATION_C9D3E4",
"record_type": "validation_decision",
"record_status": "approved",
"record_metadata": {
"created_by": "validation_agent",
"created_at": "2026-02-06T10:30:18.789Z",
"version": "0.1.0",
"source_plan_id": "PLAN_F5B2C7"
},
"eago_version": "0.1.0",
"message_type": "validation_decision",
"validation_id": "VALIDATION_G6C3B8",
"plan_id": "PLAN_F5B2C7",
"contract_id": "CONTRACT_E7D3A1",
"timestamp": "2026-02-06T10:30:18.789Z",
// Context Propagation
"context_hierarchy": {
"session_context": {
"session_id": "sess_20260206_1030_001",
"user_id": "sarah.clerk@example.com",
"client_id": "EXAMPLE_CRM_SYSTEM",
"authentication": {
"method": "mtls_spiffe",
"spiffe_id": "spiffe://eago.example.com/workload/crm-service",
"security_level": "high",
"groups": ["customer_service", "address_updaters", "uk_operations"]
}
},
"validation_context": {
"validation_id": "VALIDATION_G6C3B8",
"validation_strategy": "automated_with_monitoring",
"approval_authority": "supervisor_delegation",
"risk_tolerance": "medium"
}
},
// Validation Decision
"validation_decision": {
"decision": "approved",
"decision_reason": "Plan meets all policy requirements and risk tolerance",
"approval_type": "automated",
"approval_timestamp": "2026-02-06T10:30:18.789Z",
"valid_until": "2026-02-06T12:30:18.789Z",
"decision_confidence": 0.94
},
// Enhanced Execution Conditions
"execution_conditions": {
"mandatory_monitoring": [
{
"condition_id": "MONITOR_001",
"type": "real_time_cost_tracking",
"threshold": {"cost_increase": 0.20},
"action": "pause_and_review"
},
{
"condition_id": "MONITOR_002",
"type": "agent_performance_tracking",
"threshold": {"failure_rate": 0.05},
"action": "activate_fallback"
}
],
"optional_enhancements": [
{
"enhancement_id": "ENHANCE_001",
"type": "enhanced_audit_logging",
"reason": "PII processing requires detailed audit trail"
}
],
"escalation_triggers": [
{
"trigger_id": "ESCALATE_001",
"condition": "cost_overrun_15_percent",
"action": "pause_and_escalate_to_human"
}
]
},
// Approved Execution Parameters
"approved_execution_plan": {
"max_execution_time_sec": 600,
"max_cost_usd": 12.00,
"max_acu": 4.0,
"required_monitoring_level": "enhanced",
"audit_requirements": {
"real_time_logging": true,
"blockchain_anchoring": true,
"compliance_reporting": true
},
"performance_requirements": {
"min_success_rate": 0.95,
"max_failure_rate": 0.05,
"required_uptime": 0.99
}
},
// Compliance Validation Results
"compliance_validation": {
"regulatory_compliance": {
"gdpr_compliance": "validated",
"ccpa_compliance": "validated",
"psd2_compliance": "validated",
"data_residency_compliance": "validated_with_warning"
},
"policy_compliance": {
"financial_policies": "passed",
"security_policies": "passed",
"operational_policies": "passed",
"business_policies": "passed"
},
"risk_acceptance": {
"financial_risk": "accepted",
"operational_risk": "accepted_with_monitoring",
"compliance_risk": "accepted",
"security_risk": "accepted",
"availability_risk": "accepted"
}
},
// Audit Trail
"audit_trail": {
"validation_steps": [
{
"step": "policy_evaluation",
"timestamp": "2026-02-06T10:30:18.123Z",
"duration_ms": 45,
"result": "passed"
},
{
"step": "risk_assessment",
"timestamp": "2026-02-06T10:30:18.168Z",
"duration_ms": 32,
"result": "medium_risk_accepted"
},
{
"step": "cost_approval",
"timestamp": "2026-02-06T10:30:18.200Z",
"duration_ms": 15,
"result": "approved_under_delegation"
},
{
"step": "compliance_verification",
"timestamp": "2026-02-06T10:30:18.215Z",
"duration_ms": 28,
"result": "compliant_with_warnings"
}
],
"immutable_hash": "sha256:a7b8c9d2e3f4g5h6i7j8k9l0m1n2o3p4q5r6s7t8u9v0w1x2y3z4",
"blockchain_anchor": {
"block_hash": "0x1234567890abcdef",
"transaction_id": "0xabcdef1234567890",
"timestamp": "2026-02-06T10:30:19.000Z"
}
}
}
}Auto-Approval Criteria:
- Risk score ≤ 0.30 (Low risk)
- Cost ≤ $10.00 or within delegated authority
- All policy compliance checks passed
- No regulatory violations detected
- Standard data classification (non-sensitive)
Human Review Triggers:
- Risk score > 0.60 (High risk)
- Cost > delegated authority limit
- Policy compliance failures
- New regulatory jurisdiction
- Sensitive data processing (PHI, financial)
- Cross-border data transfers
Escalation Levels:
- Automatic Approval: Low risk, standard operations
- Supervisor Review: Medium risk, moderate cost
- Manager Approval: High cost, complex compliance
- Director Approval: High risk, significant impact
- Board Approval: Critical risk, major compliance implications
Review Criteria:
- Financial Impact: Total cost and budget implications
- Risk Assessment: Comprehensive risk evaluation
- Compliance Impact: Regulatory and policy implications
- Business Impact: Operational and strategic considerations
The Validation Agent outputs a comprehensive validation decision that includes:
Core Decision Components:
- Validation Decision: Approved/Rejected/Conditionally Approved/Escalated
- Execution Conditions: Mandatory monitoring, optional enhancements, escalation triggers
- Approved Parameters: Maximum cost, time, and performance requirements
- Compliance Validation: Regulatory and policy compliance confirmation
- Audit Trail: Immutable record of validation process and decisions
Context Propagation:
- Session Context: Maintained user and authentication context
- Validation Context: Approval authority, risk tolerance, monitoring requirements
Monitoring Requirements:
- Real-time Tracking: Cost, performance, and compliance monitoring
- Alerting: Threshold-based notifications and escalations
- Reporting: Compliance and audit reporting requirements
Handoff to Execution Agent:
Validation Agent → [Validation Decision] → Execution Agent → [Orchestrated Execution] → Response
Execution Guidelines (provided to Execution Agent):
- Approved execution parameters and constraints
- Mandatory monitoring and reporting requirements
- Escalation triggers and response procedures
- Performance and compliance thresholds
- Audit trail and logging requirements
Execution Outcomes:
- Proceed: Execute plan within approved parameters
- Monitor: Execute with enhanced monitoring and reporting
- Escalate: Pause execution and request human review
- Reject: Block execution due to validation failures
Validation Performance Metrics:
- Validation Latency: Time to complete validation assessment
- Decision Accuracy: Percentage of correct validation decisions
- Policy Coverage: Percentage of applicable policies evaluated
- Risk Prediction: Accuracy of risk assessments vs. actual outcomes
- Compliance Rate: Percentage of validated plans meeting compliance requirements
Optimization Techniques:
- Policy Caching: Cache frequently accessed policies and rules
- Parallel Evaluation: Execute independent validation checks concurrently
- Machine Learning: Improve risk assessment based on historical outcomes
- Dynamic Thresholds: Adjust approval thresholds based on performance data
Validation Failure Scenarios:
- Policy Violations: Plans violating organizational policies
- Regulatory Non-compliance: Plans not meeting regulatory requirements
- Risk Threshold Exceeded: Plans exceeding acceptable risk levels
- Cost Approval Denied: Plans exceeding authorized budget limits
- Technical Failures: Validation system or database unavailability
Resilience Strategies:
- Graceful Degradation: Fallback to manual approval processes
- Redundant Systems: Multiple validation engines for critical decisions
- Audit Continuity: Maintain audit trails during system failures
- Emergency Procedures: Fast-track approvals for critical operations
The Validation Agent serves as the governance and compliance gatekeeper of the OpenEAGO system:
Core Responsibilities:
- Policy Compliance: Verify adherence to organizational policies and procedures
- Risk Assessment: Evaluate and score operational, financial, and compliance risks
- Cost Approval: Validate budget constraints and authorize expenditures
- Regulatory Verification: Ensure compliance with applicable regulatory frameworks
- Human Approval: Route complex decisions to appropriate approval authorities
- Audit Trail: Maintain immutable records of all validation decisions
Key Algorithms:
- Policy Engine: Rule-based evaluation of organizational policies
- Risk Engine: Multi-factor risk assessment and scoring
- Approval Engine: Automated decision-making with human escalation
- Compliance Engine: Regulatory framework validation and verification
Integration Points:
- Input: Execution plans from Planning Agent
- Output: Validation decisions for Execution Agent
- Context: Enhanced validation context for execution tracking
- Security: Multi-level approval workflows and audit trail maintenance
The Validation Agent ensures that all execution plans meet organizational governance requirements, regulatory compliance standards, and risk tolerance levels before proceeding to execution, maintaining the integrity and compliance of the OpenEAGO system.